Intelligent Systems for Telecare

1. Intelligent Systems for Telecare Basim Majeed, Ben Azvine, Steve Brown (BT) Trevor Martin (University of Bristol)

2. Presentation Outline • Introduction • Centre for Care in the Community • Well-being Monitoring • Intelligent Data Analysis • Questions

3. IntroductionEffects of the demographic shift 4.5 50 Support Ratio 1 UK Long TermHealthcare Cost 2 45 4.0 40 35 3.5 UK Long Term Healthcare Cost (£B) Ratio Persons Aged 16-64 to 65+ 30 3.0 25 20 2.5 15 2.0 10 1995 2005 2015 2025 2035 2045 2055 Year 1. Office for National Statistics, 2002.2. Royal Commission Report into Long Term Care, 1999.

4. Introduction • Intelligent Telecare provides new ways of enabling elderly or vulnerable people to maintain their independence and live in their own homes for longer. • BT is leading a DTI sponsored group of academic partners to develop an Intelligent monitoring system for telecare. • System uses a range of low cost sensors placed in the home to monitor a person's activity and build up a picture of their behaviour. • Target users are care professionals within social services (initially at Liverpool).

5. DTI/BT Funded Centre for Care in the Community Three Research Teams • Domain Specific Modelling • Sensor Network • Intelligent Data Analysis

6. Well-being monitoringIdentifying the activities

7. Identifying the activities to monitor • Older population split into ‘priority groups’ identified by the DoH • Relevant activities identified for each priority group • A core set of activities relevant to physical, mental and social elements of well-being has been identified • Specific questions for each activity have been identified

8. Leaving & returning home (Social interaction) Visitors (Social interaction) Preparing food & eating appropriately (*ADL’s) Sleeping (*ADL’s) Leisure activities (Personal goals) Personal appearance (Personal goals) Core activity monitoring set for the physically frail priority group *Activities of Daily Living

9. Master bedroom Client home Kitchen sink drainer Double bed Back door Wardrobes Gas Oven/hob Bath Basin Fridge/ freezer Landing RMU Bathroom cupboard Lounge W.C. Coffee table Spare bedroom Draws ‘Radio’ chair Fireplace Pile of various objects Wardrobes ‘TV’’ chair Sofa & armchairs TV Window sill

10. Intelligent Data AnalysisConverting sensor data into Activity information

11. Domain Knowledge Data from a Sensor Network Target Group Well-being concepts Sleep monitoring Visitor detection ……. System Overview Provide Answers to Core Activity Questions

12. The Challenge • People are different, fickle, unpredictable, and unlike physical systems that have known responses to external influences • The challenge is to provide a system that can adapt to changes • These characteristics are very important in well-being monitoring, more so than e.g. consumer behaviour analysis • Cause No Harm! • Our Approach: Assist the carer in decision making by providing an easy-to-configure system that hides the complexity of the analysis

13. User Interface Sensor Object Management Interface to Sensor Network Report Generator Sensor Information AnalysisAlgorithms Pre-Processed Data Report Data Raw Data Pre-processing System Overview

14. Raw Data: • Contains logical inconsistencies • Subject to intermittent errors • Huge amount of events captured • Decision making needs abstract data: • when, where and what

15. User Interface Sensor Object Management Report Generator Sensor Information AnalysisAlgorithms Pre-Processed Data Report Data Raw Data Pre-processing Interface to Sensor Network Pre-processing Level - I • Grouping of similar events into blocks • Categorising sensors into location and activity types • Capturing additional sensor information e.g. activity levels, silence • Abnormal toggle sensor detection • Sensor location consistency check

16. User Interface Sensor Object Management Report Generator Sensor Information AnalysisAlgorithms Pre-Processed Data Report Data Raw Data Pre-processing Interface to Sensor Network Pre-processing Level - II • Using Fuzzy values for start time and duration of sensor event blocks • Converts time axis into a more meaningful and manageable number of regions • Allows reasoning with Fuzzy rules • Duration membership functions are learnt for each sensor and each client Bed Occupancy Kitchen Occupancy

17. User Interface Sensor Object Management Report Generator Sensor Information AnalysisAlgorithms Pre-Processed Data Report Data Raw Data Pre-processing Interface to Sensor Network Analysis Techniques • Provide answers to core activity questions • Abstraction of sensor events into daily activities • Identify key points in the data sequence (e.g. data silences) • Analyse surrounding sensor data to classify activities (e.g. bed in use - asleep) • Trend analysis

18. Activity Specific Analysis • Visitor Activity: • Challenge: No identification sensors (non-intrusive sensing) • To infer the existence of visitors: • Various metrics are used to describe regions of activity • Activity Levels • Delay in activity level changes • Non-adjacent rooms activity • Regions between entrance events are then compared • Changes are used to accumulate evidence of visitor activity

19. Region B clearly has a higher rate of activity • Sliding a window W across region B shows that the change in activity level occurs at the beginning of region B Visitor Activity • Use of Activity Levels for visitor evidence accumulation: • Average level of activity between door events • The delay after a door event before behaviours show change

20. All Room Changes Non-Adjacent Changes Non-Adjacent Bursts Visitor Activity • A Changing Rooms measure comprises: • The rate at which the location of activity changes • The proportion of changes between non-adjacent rooms • The longest sequence of consecutive non-adjacent changes • Fuzzy rules are used to accumulate the evidence of a visit

21. Visitor Activity Negative and Positive Changes Combined Sets (Equal Data Share) Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Big Fall Fall Steady Rise Big Rise A B C D E F A C D E F -1 0 0 1 -1 1 Act. Level Change Act. Level Change Activity Level Change Initial Sets (Equally Sized) Final Sets (Averaged) Big Fall Fall Steady Rise Big Rise Big Fall Fall Steady Rise Big Rise -1 1 -1 1 Activity Level Change Activity Level Change Training to obtain fuzzy membership functions • At each door event in the training set the changes in activity level are recorded • The results are split into ordered negative and positive changes • Each half is split into three, using their 1st & 3rd quartiles to define fuzzy sets • The fuzzy sets of each half are combined, then averaged with initial sets

22. Visitor Activity • Important Notes: • Data splits are {A: 40%, B: 40%, C: 20%}, {D: 20%, E: 40%, F: 40%} • Equally sized sets are not sensitive to an individual’s data spread • Sets using equal data can be over-sensitive when data spread is uneven • Averaged sets used are a compromise, avoiding both problems • The same style of training is applied to the changing rooms measure

23. User GUI

24. User GUI

25. Deployment DB Thin client SQL High Level Application objects over HTTP App. Server Client - Web browser based - Thin client - Multi-user - Pure HTTP/HTTPS communication Back End - Extensive calculation on server   - SQL database driven